Scalable one-pass multi-view clustering with tensorized multiscale bipartite graphs fusion

• Published in: Neural networks Vol. 190; p. 107669
• Main Authors: Wang, Fei, Lu, Gui-Fu
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2025
• Subjects: Algorithms; Bipartite graph; Cluster Analysis; Humans; Large-scale dataset; Multi-view clustering; Neural Networks, Computer; Multi-view clustering; Bipartite graph; Large-scale dataset
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2025.107669

In the existing multi-view clustering task, anchor-based methods are widely used for large-scale data processing to reduce computational complexity and achieve satisfactory results. However, most existing anchor-based algorithms generate a single-scale bipartite graph for each view, limiting a more accurate representation of the original data. Moreover, these algorithms typically require further clustering processing, and the contribution of each view to the final clustering result is static, lacking dynamic adjustment based on the data characteristics. To address the above issues, we introduce an innovative multi-view clustering method called Scalable One-pass Multi-View Clustering with Tensorized Multiscale Bipartite Graphs Fusion (SOMVC/TMBGF). Specifically, we initially generate multiple scales of bipartite graphs for each view and adaptively fuse them to obtain a partition matrix, thereby fully leveraging the structural information of the original data for a more accurate representation. Subsequently, we combine the partition matrices from each view into a tensor constrained with Tensor Schatten p-norm, capturing the higher-order correlations and complementary information between views. Finally, to enhance clustering performance, we integrate partition matrix learning and clustering into a unified framework, dynamically adjusting the contribution of each view’s partition matrix through weighted spectral rotation to obtain the final clustering result. Experimental results show that SOMVC/TMBGF outperforms existing methods significantly in both clustering performance and computational efficiency, demonstrating its advantage in handling large-scale multi-view data.